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A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

CAAI Transactions on Intelligence Technology 2024年第4期9卷 790-804页

作者： Nechirvan Asaad Zebari Chira Nadheef mohammed Dilovan Asaad Zebari mazin abed mohammed Diyar Qader Zeebaree Haydar Abdulameer Marhoon Karrar Hameed Abdulkareem Seifedine Kadry Wattana Viriyasitavat Jan Nedoma Radek Martinek Department of Information Technology Lebanese French UniversityErbilIraq Department of Computer Science University of ZakhoZakhoKurdistan RegionIraq Department of Computer Science College of ScienceNawroz UniversityDuhokKurdistan RegionIraq Department of Artificial Intelligence College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq Department of Telecommunications VSB‐Technical University of OstravaOstravaCzech Republic Department of Cybernetics and Biomedical Engineering VSB‐Technical University of OstravaOstravaCzech Republic Department of Computer Network and Information Security Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Information and Communication Technology Research Group Scientific Research CenterAl‐Ayen UniversityThi‐QarIraq College of Computer Sciences and Information Technology University of KerbalaKarbalaIraq College of Agriculture Al‐Muthanna UniversitySamawahIraq Department of Applied Data Science Noroff University CollegeKristiansandNorway Faculty of Commerce and Accountancy Chulalongkorn Business SchoolChulalongkorn UniversityBangkokThailand

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

关键词： brain tumour deep learning feature fusion model MRI images multi‐classification

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Formulating layered adjustable autonomy for unmanned aerial vehicles

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International Journal of Intelligent Computing and Cybernetics 2017年第4期10卷 430-450页

作者： Salama A.Mostafa Mohd Sharifuddin Ahmad Aida Mustapha mazin abed mohammed Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaBatu PahatMalaysia College of Information Technology Universiti Tenaga NasionalKajangMalaysia Planning and Follow Up Department University of AnbarAnbarIraq

Purpose–The purpose of this paper is to propose a layered adjustable autonomy(LAA)as a dynamically adjustable autonomy model for a multi-agent *** is mainly used to efficiently manage humans’and agents’shared control of autonomous systems and maintain humans’global control over the ***/methodology/approach–The authors apply the LAA model in an agent-based autonomous unmanned aerial vehicle(UAV)*** UAV system implementation consists of two parts:software and *** software part represents the controller and the cognitive,and the hardware represents the computing machinery and the actuator of the UAV *** UAV system performs three experimental scenarios of dance,surveillance and search *** selected scenarios demonstrate different behaviors in order to create a suitable test plan and ensure significant ***–The results of the UAV system tests prove that segregating the autonomy of a system as multidimensional and adjustable layers enables humans and/or agents to perform actions at convenient autonomy ***,reducing the adjustable autonomy drawbacks of constraining the autonomy of the agents,increasing humans’workload and exposing the system to ***/value–The application of the LAA model in a UAV manifests the significance of implementing dynamic adjustable *** the autonomy within three phases of agents run cycle(taskselection,actions-selection and actions-execution)is an original idea that aims to direct agents’autonomy toward performance *** agents’abilities are well exploited when an incompetent agent switches with a more competent one.

关键词： Unmanned aerial vehicle Multi-agent system Adjustable autonomy Autonomous agent

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Predicting the Type of Crime: Intelligence Gathering and Crime Analysis

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Computers, Materials & Continua 2021年第3期66卷 2317-2341页

作者： Saleh Albahli Anadil Alsaqabi Fatimah Aldhubayi Hafiz Tayyab Rauf Muhammad Arif mazin abed mohammed Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Science University of GujratGujratPakistan School of Computer Science Guanzghou UniversityGuangzhou510006China College of Computer Science and Information Technology University of AnbarRamadiIraq

Crimes are expected to rise with an increase in population and the rising gap between society’s income *** contribute to a significant portion of the socioeconomic loss to any society,not only through its indirect damage to the social fabric and peace but also the more direct negative impacts on the economy,social parameters,and reputation of a *** and other preventive resources are limited and have to be *** conventional methods are being superseded by more modern approaches of machine learning algorithms capable of making predictions where the relationships between the features and the outcomes are *** it possible for such algorithms to provide indicators of specific areas that may become criminal *** predictions can be used by policymakers and police personals alike to make effective and informed strategies that can curtail criminal activities and contribute to the nation’s *** paper aims to predict factors that most affected crimes in Saudi Arabia by developing a machine learning model to predict an acceptable output *** results show that FAMD as features selection methods showed more accuracy on machine learning classifiers than the PCA *** naïve Bayes classifier performs better than other classifiers on both features selections methods with an accuracy of 97.53%for FAMD,and PCA equals to 97.10%.

关键词： Prediction machine learning crime prevention naïve bayes crime prediction classification algorithms

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Statistical Medical Pattern Recognition for Body Composition Data Using Bioelectrical Impedance Analyzer

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Computers, Materials & Continua 2021年第5期67卷 2601-2617页

作者： Florin Valentin Leuciuc Maria Daniela Craciun Iulian Stefan Holubiac mazin abed mohammed Karrar Hameed Abdulkareem Gheorghe Pricop Stefan cel Mare University of Suceava Suceava720229Romania The Interdisciplinary Research Center for Human Motricity and Health Sciences Suceava720229Romania College of Computer Science and Information Technology University of AnbarRamadi31001Iraq College of Agriculture Al-Muthanna UniversitySamawah66001Iraq

Identifying patterns,recognition systems,prediction methods,and detection methods is a major challenge in solving different medical *** categories of devices for personal and professional assessment of body composition are *** impedance analyzer is a simple,safe,affordable,mobile,non-invasive,and less expensive alternative device for body composition *** the body composition pattern of different groups with varying age and gender is a major challenge in defining an optimal level because of the body shape,body mass,energy requirements,physical fitness,health status,and metabolic ***,this research aims to identify the statistical medical pattern recognition of body composition data by using a bioelectrical impedance *** previous studies,a pattern was identified for four indicators that concern body composition(e.g.,body mass index(BMI),body fat,muscle mass,and total body water).The novelty of our study is the fact that we identified a recognition pattern by using medical statistical methods for a body composition that contains seven indicators(e.g.,body fat,visceral fat,BMI,muscle mass,skeletal muscle mass,sarcopenic index,and total body water).The youth that exhibited the body composition pattern identified in our study could be considered *** deviation of one or more parameters outside the margins of the pattern for body composition could be associated with health issues,and more medical investigations would be needed for a *** is considered a valid and reliable device to assess body composition along with medical statistical methods to identify a pattern for body composition according to the age,gender,and other relevant parameters.

关键词： Statistical method pattern recognition body composition assessment

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COVID-19 Public Sentiment Insights: A Text Mining Approach to the Gulf Countries

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Computers, Materials & Continua 2021年第5期67卷 1613-1627页

作者： Saleh Albahli Ahmad Algsham Shamsulhaq Aeraj Muath Alsaeed Muath Alrashed Hafiz Tayyab Rauf Muhammad Arif mazin abed mohammed Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Science Univesity of GurjatPakistan School of Computer Science Guanzghou UniversityGuangzhou510006China College of Computer Science and Information technology University of Anbar11RamadiAnbarIraq

Social media has been the primary source of information from mainstream news agencies due to the large number of users posting their *** COVID-19 outbreak did not only bring a virus with it but it also brought fear and uncertainty along with inaccurate and misinformation spread on social media *** phenomenon caused a state of panic among *** studies were conducted to stop the spread of fake news to help people cope with the *** this paper,a semantic analysis of three levels(negative,neutral,and positive)is used to gauge the feelings of Gulf countries towards the pandemic and the lockdown,on basis of a Twitter dataset of 2 months,using Natural Language Processing(NLP)*** has been observed that there are no mixed emotions during the pandemic as it started with a neutral reaction,then positive sentiments,and lastly,peaks of negative *** results show that the feelings of the Gulf countries towards the pandemic depict approximately a 50.5%neutral,a 31.2%positive,and an 18.3%negative sentiment *** study can be useful for government authorities to learn the discrepancies between different populations from diverse areas to overcome the COVID-19 spread accordingly.

关键词： COVID-19 sentiment analysis natural language processing twitter social data mining sentiment polarity

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Parametric Methods for the Regional Assessment of Cardiac Wall Motion Abnormalities: Comparison Study

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Computers, Materials & Continua 2021年第10期69卷 1233-1252页

作者： Narjes Benameur mazin abed mohammed Ramzi Mahmoudi Younes Arous Begonya Garcia-Zapirain Karrar Hameed Abdulkareem Mohamed Hedi Bedoui University of Tunis El Manar Higher Institute of Medical Technologies of TunisLaboratory of Biophysics and Medical TechnologiesTunisTunisia College of Computer Science and Information Technology University of Anbar31001AnbarIraq Faculty of Medicine of Monastir Medical Imaging Technology Lab-LTIM-LR12ES06University of Monastir5019MonastirTunisia Gaspard-Monge Computer-Science Laboratory Paris-Est UniversityMixed Unit CNRS-UMLV-ESIEE UMR8049BP99ESIEE Paris CitéDescartes93162 Noisy Le GrandFrance Military Hospital of Instruction of Tunis TunisTunisia eVIDA Lab University of DeustoAvda/Universidades 24Bilbao48007Spain College of Agriculture Al-Muthanna UniversitySamawah66001Iraq

Left ventricular(LV)dysfunction is mainly assessed by global contractile indices such as ejection fraction and LV Volumes in cardiac *** these indices give information about the presence or not of LV alteration,they are not able to identify the location and the size of such *** aim of this study is to compare the performance of three parametric imaging techniques used in cardiac MRI for the regional quantification of cardiac *** proposed approaches were evaluated on 20 patients with myocardial infarction and 20 subjects with normal *** parametric images approaches:covariance analysis,parametric images based on Hilbert transform and those based on the monogenic signal were evaluated using cine-MRI frames acquired in three planes of *** results show that parametric images generated from the monogenic signal were superior in term of sensitivity(89.69%),specificity(86.51%)and accuracy(89.06%)to those based on covariance analysis and Hilbert transform in the detection of contractile dysfunction related to myocardial ***,the parametric image based on the monogenic signal is likely to provide additional regional indices about LV dysfunction and it may be used in clinical practice as a tool for the analysis of the myocardial alterations.

关键词： Covariance analysis cardiac MRI monogenic signal assessment Hilbert transform

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Novel Universal Windowing Multicarrier Waveform for 5G Systems

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Computers, Materials & Continua 2021年第5期67卷 1523-1536页

作者： Ahmed Hammoodi Lukman Audah Montadar Abas Taher mazin abed mohammed Mustafa S.Aljumaily Adeeb Salh Shipun A.Hamzah Wireless and Radio Science Centre Faculty of Electrical and Electronic EngineeringUniversiti Tun Hussein Onn MalaysiaParit Raja86400Malaysia Department Communications Engineering University of DiyalaBaqubah32001Iraq College of Computer Science and Information Technology University of AnbarAnbar31001Iraq Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN37901USA

Fifth Generation(5G)systems aim to improve flexibility,coexistence and diverse service in several aspects to achieve the emerging applications *** and filtering of the traditional multicarrier waveforms are now considered common sense when designing more flexible *** paper proposed a Universal Windowing Multi-Carrier(UWMC)waveform design platform that is flexible,providing more easily coexists with different pulse shapes,and reduces the Out of Band Emissions(OOBE),which is generated by the traditional multicarrier methods that used in the previous generations of the mobile *** novel proposed approach is different from other approaches that have been proposed,and it is based on applying a novel modulation approach for the Quadrature-Amplitude Modulation(64-QAM)which is considered very popular in mobile *** new approach is done by employing flexible pulse shaping windowing,by assigning windows to various *** leads to decreased side-lobes,which are going to reduce OOBE and boost the spectral efficiency by assigning them to edge subscribers *** new subband windowing(UWMC)will also maintain comprehensively the non-orthogonality by a variety of windowing and make sure to keep window time the same for all *** addition,this paper shows that the new approach made the Bit Error Rate(BER)equal to the conventional Windowed-Orthogonal Frequency Division Multiplexing(W-OFDM).This platform achieved great improvement for some other Key Performance Indicators(KPI),such as the Peak to Average Power Ratio(PAPR)compared with the conventional(W-OFDM)and the conventional Universal Filtered Multicarrier(UFMC)*** particular,the proposed windowing scheme outperforms previous designs in terms of the Power Spectral Density(PSD)by 58%and the(BER)by 1.5 dB and reduces the Complementary Cumulative Distribution Function Cubic Metric(CCDF-CM)by 24%.

关键词： 5G waveform window-OFDM universal filtered multi-carrier key performance indicators

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Fully Automatic Segmentation of Gynaecological Abnormality Using a New Viola–Jones Model

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Computers, Materials & Continua 2021年第3期66卷 3161-3182页

作者： Ihsan Jasim Hussein M.A.Burhanuddin mazin abed mohammed Mohamed Elhoseny Begonya Garcia-Zapirain Marwah Suliman Maashi Mashael S.Maashi BIOCORE Research Group Universiti Teknikal Malaysia MelakaMelaka76100Malaysia Director of UTeM International Centre BIOCORE Research GroupUniversiti Teknikal Malaysia MelakaMelaka76100Malaysia College of Computer Science and Information Technology University of AnbarRamadi31001Iraq Department of Computer Science College of Computer Information TechnologyAmerican University in the Emirates503000United Arab Emirates eVIDA Laboratory University of DeustoBilbao48007Spain Medical Laboratory Science Department King Abdulaziz UniversityJeddah21589Saudi Arabia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia

One of the most complex tasks for computer-aided diagnosis(Intelligent decision support system)is the segmentation of ***,this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound *** main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer *** addition,proposed an approach that can efficiently generate region-of-interest(ROI)and new features that can be used in characterizing lesion *** study uses two databases in training and testing the proposed segmentation *** breast cancer database contains 250 images,while that of the ovarian tumor has 100 images obtained from several hospitals in *** of the experiments showed that the proposed approach demonstrates better performance compared with those of other segmentation methods used for segmenting breast and ovarian ultrasound *** segmentation result of the proposed system compared with the other existing techniques in the breast cancer data set was 78.8%.By contrast,the segmentation result of the proposed system in the ovarian tumor data set was 79.2%.In the classification results,we achieved 95.43%accuracy,92.20%sensitivity,and 97.5%specificity when we used the breast cancer data *** the ovarian tumor data set,we achieved 94.84%accuracy,96.96%sensitivity,and 90.32%specificity.

关键词： Viola-Jones model breast cancer segmentation ovarian tumor ovarian tumor segmentation breast cancer ultrasound images active contour cascade model

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A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis

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Computers, Materials & Continua 2021年第4期67卷 51-71页

作者： Mohamed Elhoseny mazin abed mohammed Salama A.Mostafa Karrar Hameed Abdulkareem Mashael S.Maashi Begonya Garcia-Zapirain Ammar Awad Mutlag Marwah Suliman Maashi Department of Computer Science College of Computer Information TechnologyAmerican University in the Emirates503000United Arab Emirates College of Computer Science and Information Technology University of AnbarRamadi31001Iraq Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaJohor86400Malaysia College of Agriculture Al-Muthanna UniversitySamawah66001Iraq Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia eVIDA Lab University of DeustoBilbao48007Spain Department of Pure Science Ministry of EducationGeneral Directorate of CurriculaBaghdadIraq Department of Medical Laboratory Science Faculty of Applied Medical ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia

Heart disease(HD)is a serious widespread life-threatening *** heart of patients with HD fails to pump sufcient amounts of blood to the entire *** the occurrence of HD early and efciently may prevent the manifestation of the debilitating effects of this disease and aid in its effective *** methods for diagnosing HD are sometimes unreliable and insufcient in analyzing the related *** an alternative,noninvasive medical procedures based on machine learning(ML)methods provide reliable HD diagnosis and efcient prediction of HD ***,the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classication features from patients with *** this study,we propose an automated heart disease diagnosis(AHDD)system that integrates a binary convolutional neural network(CNN)with a new multi-agent feature wrapper(MAFW)*** MAFW model consists of four software agents that operate a genetic algorithm(GA),a support vector machine(SVM),and Naïve Bayes(NB).The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classication.A nal tuning to CNN is then performed to ensure that the best set of features are included in HD *** CNN consists of ve layers that categorize patients as healthy or with HD according to the analysis of optimized HD *** evaluate the classication performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using across-validation technique and by assessing six evaluation *** AHDD system achieves the highest accuracy of 90.1%,whereas the other ML and conventional CNN models attain only 72.3%–83.8%accuracy on ***,the AHDD system proposed herein has the highest capability to identify patients with *** system can be used by medical

关键词： Heart disease machine learning multi-agent feature wrapper model heart disease diagnosis HD cleveland datasets convolutional neural network

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Artificial Intelligence Based Sentence Level Sentiment Analysis of COVID-19

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Computer Systems Science & Engineering 2023年第10期47卷 791-807页

作者： Sundas Rukhsar Mazhar Javed Awan Usman Naseem Dilovan Asaad Zebari mazin abed mohammed Marwan Ali Albahar mohammed Thanoon Amena Mahmoud Department of Software Engineering University of Management and TechnologyLahore54770Pakistan School of Computer Science The University of SydneySydneyAustralia Department of Computer Science College of ScienceNawroz UniversityDuhok42001Kurdistan RegionIraq College of Computer Science and Information Technology University of AnbarAnbar31001Iraq Department of Computer Science Umm Al Qura UniversityMecca24211Saudi Arabia Computer Science Department Faculty of Computers and InformationKafrelsheikh UniversityKafrelsheikhEgypt

Web-blogging sites such as Twitter and Facebook are heavily influenced by emotions,sentiments,and data in the modern ***,a widely used microblogging site where individuals share their thoughts in the form of tweets,has become a major source for sentiment *** recent years,there has been a significant increase in demand for sentiment analysis to identify and classify opinions or expressions in text or *** or expressions of people about a particular topic,situation,person,or product can be identified from sentences and divided into three categories:positive for good,negative for bad,and neutral for mixed or confusing *** process of analyzing changes in sentiment and the combination of these categories is known as“sentiment analysis.”In this study,sentiment analysis was performed on a dataset of 90,000 tweets using both deep learning and machine learning *** deep learning-based model long-short-term memory(LSTM)performed better than machine learning *** short-term memory achieved 87%accuracy,and the support vector machine(SVM)classifier achieved slightly worse results than LSTM at 86%.The study also tested binary classes of positive and negative,where LSTM and SVM both achieved 90%accuracy.

关键词： COVID-19 artificial intelligence machine learning deep learning sentimental analysis support vector classifier

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